Overview

Dataset statistics

Number of variables55
Number of observations179
Missing cells1032
Missing cells (%)10.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory228.8 KiB
Average record size in memory1.3 KiB

Variable types

Categorical32
DateTime2
Numeric20
Boolean1

Dataset

DescriptionJHB_EZIN_025 - Quality-corrected harmonized data
CreatorRP2 Clinical Data Quality Team
AuthorQuality-Checked Data
URLHEAT Research Projects

Variable descriptions

Age (at enrolment)Patient age at study enrollment
SexBiological sex
RaceRacial/ethnic group
CD4 cell count (cells/µL)CD4+ T lymphocyte count (missing codes removed)
HIV viral load (copies/mL)HIV RNA copies per mL (missing codes removed)
Antiretroviral Therapy StatusCurrent ART status
BMI (kg/m²)Body Mass Index (extreme values removed)
Waist circumference (cm)Waist circumference (corrected from mm to cm)
weight_kgBody weight in kilograms
height_mHeight in meters
Hematocrit (%)Hematocrit (zero values removed)
hemoglobin_g_dLHemoglobin concentration
White blood cell count (×10³/µL)Total WBC count
Red blood cell count (×10⁶/µL)Total RBC count
Platelet count (×10³/µL)Platelet count (missing codes removed)
MCV (MEAN CELL VOLUME)Mean corpuscular volume
mch_pgMean corpuscular hemoglobin
mchc_g_dLMean corpuscular hemoglobin concentration
RDWRed cell distribution width
Lymphocyte count (×10⁹/L)Lymphocyte absolute count (corrected labeling)
Neutrophil count (×10⁹/L)Neutrophil absolute count (corrected labeling)
Monocyte count (×10⁹/L)Monocyte absolute count (corrected labeling)
Eosinophil count (×10⁹/L)Eosinophil absolute count (corrected labeling)
Basophil count (×10⁹/L)Basophil absolute count (corrected labeling)
ALT (U/L)Alanine aminotransferase (missing codes removed)
AST (U/L)Aspartate aminotransferase
Alkaline phosphatase (U/L)Alkaline phosphatase
Total bilirubin (mg/dL)Total bilirubin
Albumin (g/dL)Serum albumin
Total protein (g/dL)Total serum protein
creatinine_umol_LSerum creatinine
creatinine clearanceEstimated creatinine clearance
Sodium (mEq/L)Serum sodium
Potassium (mEq/L)Serum potassium
fasting_glucose_mmol_LFasting blood glucose
total_cholesterol_mg_dLTotal cholesterol
hdl_cholesterol_mg_dLHDL cholesterol
ldl_cholesterol_mg_dLLDL cholesterol
Triglycerides (mg/dL)Triglycerides
systolic_bp_mmHgSystolic blood pressure
diastolic_bp_mmHgDiastolic blood pressure
heart_rate_bpmHeart rate (zero values removed)
Respiratory rate (breaths/min)Respiratory rate
Oxygen saturation (%)Oxygen saturation
body_temperature_celsiusBody temperature
climate_daily_mean_tempDaily mean temperature
climate_daily_max_tempDaily maximum temperature
climate_temp_anomalyTemperature anomaly from baseline
climate_heat_day_p90Heat day indicator (>90th percentile)
climate_heat_stress_indexHeat stress index
cd4_correction_appliedQuality flag: CD4 missing codes removed
final_comprehensive_fix_appliedQuality flag: Comprehensive corrections applied
waist_circ_unit_correction_appliedQuality flag: Waist circ unit corrected
sa_biomarker_standardsSouth African biomarker reference standards

Alerts

study_source has constant value "JHB_EZIN_025"Constant
latitude has constant value "-26.2041"Constant
city has constant value "Johannesburg"Constant
province has constant value "Gauteng"Constant
country has constant value "South Africa"Constant
Country has constant value "South Africa"Constant
coordinate_precision has constant value "high"Constant
geographic_source has constant value "harmonized_datasets"Constant
HEAT_STRESS_RISK_CATEGORY has constant value "LOW"Constant
HIV_status has constant value "Positive"Constant
johannesburg_metro_valid has constant value "1.0"Constant
study_site_location has constant value "Johannesburg (General)"Constant
climate_heat_day_p90 has constant value "0.0"Constant
climate_heat_day_p95 has constant value "0.0"Constant
sa_biomarker_standards has constant value "1.0"Constant
cd4_correction_applied has constant value "0.0"Constant
final_comprehensive_fix_applied has constant value "1.0"Constant
total_protein_extreme_flag has constant value "0.0"Constant
dphru_053_final_corrections_applied has constant value "0.0"Constant
ezin_002_final_corrections_applied has constant value "0.0"Constant
quality_harmonization_version has constant value "2.0"Constant
waist_circ_unit_correction_applied has constant value "False"Constant
BMI (kg/m²) is highly overall correlated with HEAT_VULNERABILITY_SCORE and 7 other fieldsHigh correlation
HEAT_VULNERABILITY_SCORE is highly overall correlated with BMI (kg/m²) and 22 other fieldsHigh correlation
Other measures of obesity is highly overall correlated with BMI (kg/m²) and 7 other fieldsHigh correlation
Respiratory rate (breaths/min) is highly overall correlated with HEAT_VULNERABILITY_SCORE and 6 other fieldsHigh correlation
body_temperature_celsius is highly overall correlated with HEAT_VULNERABILITY_SCORE and 5 other fieldsHigh correlation
climate_14d_mean_temp is highly overall correlated with HEAT_VULNERABILITY_SCORE and 14 other fieldsHigh correlation
climate_30d_mean_temp is highly overall correlated with HEAT_VULNERABILITY_SCORE and 14 other fieldsHigh correlation
climate_7d_max_temp is highly overall correlated with HEAT_VULNERABILITY_SCORE and 15 other fieldsHigh correlation
climate_7d_mean_temp is highly overall correlated with HEAT_VULNERABILITY_SCORE and 14 other fieldsHigh correlation
climate_daily_max_temp is highly overall correlated with HEAT_VULNERABILITY_SCORE and 16 other fieldsHigh correlation
climate_daily_mean_temp is highly overall correlated with HEAT_VULNERABILITY_SCORE and 15 other fieldsHigh correlation
climate_daily_min_temp is highly overall correlated with HEAT_VULNERABILITY_SCORE and 15 other fieldsHigh correlation
climate_heat_stress_index is highly overall correlated with HEAT_VULNERABILITY_SCORE and 15 other fieldsHigh correlation
climate_p90_threshold is highly overall correlated with BMI (kg/m²) and 22 other fieldsHigh correlation
climate_p95_threshold is highly overall correlated with BMI (kg/m²) and 22 other fieldsHigh correlation
climate_p99_threshold is highly overall correlated with BMI (kg/m²) and 22 other fieldsHigh correlation
climate_season is highly overall correlated with HEAT_VULNERABILITY_SCORE and 16 other fieldsHigh correlation
climate_standardized_anomaly is highly overall correlated with HEAT_VULNERABILITY_SCORE and 15 other fieldsHigh correlation
climate_temp_anomaly is highly overall correlated with HEAT_VULNERABILITY_SCORE and 10 other fieldsHigh correlation
coordinate_source is highly overall correlated with HEAT_VULNERABILITY_SCORE and 5 other fieldsHigh correlation
heart_rate_bpm is highly overall correlated with HEAT_VULNERABILITY_SCORE and 5 other fieldsHigh correlation
height_m is highly overall correlated with HEAT_VULNERABILITY_SCORE and 5 other fieldsHigh correlation
jhb_subregion is highly overall correlated with BMI (kg/m²) and 9 other fieldsHigh correlation
longitude is highly overall correlated with BMI (kg/m²) and 9 other fieldsHigh correlation
month is highly overall correlated with HEAT_VULNERABILITY_SCORE and 6 other fieldsHigh correlation
respiration rate is highly overall correlated with HEAT_VULNERABILITY_SCORE and 6 other fieldsHigh correlation
season is highly overall correlated with HEAT_VULNERABILITY_SCORE and 16 other fieldsHigh correlation
weight_kg is highly overall correlated with BMI (kg/m²) and 7 other fieldsHigh correlation
year is highly overall correlated with HEAT_VULNERABILITY_SCORE and 13 other fieldsHigh correlation
longitude is highly imbalanced (84.6%)Imbalance
jhb_subregion is highly imbalanced (84.6%)Imbalance
respiration rate has 129 (72.1%) missing valuesMissing
Other measures of obesity has 129 (72.1%) missing valuesMissing
BMI (kg/m²) has 129 (72.1%) missing valuesMissing
Respiratory rate (breaths/min) has 129 (72.1%) missing valuesMissing
heart_rate_bpm has 129 (72.1%) missing valuesMissing
body_temperature_celsius has 129 (72.1%) missing valuesMissing
weight_kg has 129 (72.1%) missing valuesMissing
height_m has 129 (72.1%) missing valuesMissing

Reproduction

Analysis started2025-11-24 22:06:43.450561
Analysis finished2025-11-24 22:06:56.987872
Duration13.54 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

study_source
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
JHB_EZIN_025
179 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters2148
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJHB_EZIN_025
2nd rowJHB_EZIN_025
3rd rowJHB_EZIN_025
4th rowJHB_EZIN_025
5th rowJHB_EZIN_025

Common Values

ValueCountFrequency (%)
JHB_EZIN_025179
100.0%

Length

2025-11-25T00:06:57.010315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:57.043434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
jhb_ezin_025179
100.0%

Most occurring characters

ValueCountFrequency (%)
_358
16.7%
J179
8.3%
H179
8.3%
B179
8.3%
E179
8.3%
Z179
8.3%
I179
8.3%
N179
8.3%
0179
8.3%
2179
8.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1253
58.3%
Decimal Number537
25.0%
Connector Punctuation358
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
J179
14.3%
H179
14.3%
B179
14.3%
E179
14.3%
Z179
14.3%
I179
14.3%
N179
14.3%
Decimal Number
ValueCountFrequency (%)
0179
33.3%
2179
33.3%
5179
33.3%
Connector Punctuation
ValueCountFrequency (%)
_358
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1253
58.3%
Common895
41.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
J179
14.3%
H179
14.3%
B179
14.3%
E179
14.3%
Z179
14.3%
I179
14.3%
N179
14.3%
Common
ValueCountFrequency (%)
_358
40.0%
0179
20.0%
2179
20.0%
5179
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_358
16.7%
J179
8.3%
H179
8.3%
B179
8.3%
E179
8.3%
Z179
8.3%
I179
8.3%
N179
8.3%
0179
8.3%
2179
8.3%
Distinct86
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum2020-09-08 00:00:00
Maximum2021-07-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T00:06:57.081405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:57.131322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

year
Categorical

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
2021.0
148 
2020.0
31 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1074
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020.0
2nd row2020.0
3rd row2020.0
4th row2020.0
5th row2020.0

Common Values

ValueCountFrequency (%)
2021.0148
82.7%
2020.031
 
17.3%

Length

2025-11-25T00:06:57.182941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:57.318731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2021.0148
82.7%
2020.031
 
17.3%

Most occurring characters

ValueCountFrequency (%)
0389
36.2%
2358
33.3%
.179
16.7%
1148
 
13.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number895
83.3%
Other Punctuation179
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0389
43.5%
2358
40.0%
1148
 
16.5%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1074
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0389
36.2%
2358
33.3%
.179
16.7%
1148
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0389
36.2%
2358
33.3%
.179
16.7%
1148
 
13.8%

month
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4860335
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T00:06:57.352736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q37
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0434264
Coefficient of variation (CV)0.55475899
Kurtosis-0.70995305
Mean5.4860335
Median Absolute Deviation (MAD)1
Skewness0.077888098
Sum982
Variance9.2624443
MonotonicityNot monotonic
2025-11-25T00:06:57.391235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
650
27.9%
731
17.3%
129
16.2%
215
 
8.4%
1012
 
6.7%
312
 
6.7%
510
 
5.6%
99
 
5.0%
115
 
2.8%
125
 
2.8%
ValueCountFrequency (%)
129
16.2%
215
 
8.4%
312
 
6.7%
41
 
0.6%
510
 
5.6%
650
27.9%
731
17.3%
99
 
5.0%
1012
 
6.7%
115
 
2.8%
ValueCountFrequency (%)
125
 
2.8%
115
 
2.8%
1012
 
6.7%
99
 
5.0%
731
17.3%
650
27.9%
510
 
5.6%
41
 
0.6%
312
 
6.7%
215
 
8.4%

season
Categorical

High correlation 

Distinct4
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
Winter
81 
Summer
49 
Spring
26 
Autumn
23 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1074
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpring
2nd rowSpring
3rd rowSpring
4th rowSpring
5th rowSpring

Common Values

ValueCountFrequency (%)
Winter81
45.3%
Summer49
27.4%
Spring26
 
14.5%
Autumn23
 
12.8%

Length

2025-11-25T00:06:57.434967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:57.477501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
winter81
45.3%
summer49
27.4%
spring26
 
14.5%
autumn23
 
12.8%

Most occurring characters

ValueCountFrequency (%)
r156
14.5%
n130
12.1%
e130
12.1%
m121
11.3%
i107
10.0%
t104
9.7%
u95
8.8%
W81
7.5%
S75
7.0%
p26
 
2.4%
Other values (2)49
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter895
83.3%
Uppercase Letter179
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r156
17.4%
n130
14.5%
e130
14.5%
m121
13.5%
i107
12.0%
t104
11.6%
u95
10.6%
p26
 
2.9%
g26
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
W81
45.3%
S75
41.9%
A23
 
12.8%

Most occurring scripts

ValueCountFrequency (%)
Latin1074
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r156
14.5%
n130
12.1%
e130
12.1%
m121
11.3%
i107
10.0%
t104
9.7%
u95
8.8%
W81
7.5%
S75
7.0%
p26
 
2.4%
Other values (2)49
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r156
14.5%
n130
12.1%
e130
12.1%
m121
11.3%
i107
10.0%
t104
9.7%
u95
8.8%
W81
7.5%
S75
7.0%
p26
 
2.4%
Other values (2)49
 
4.6%

latitude
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.4 KiB
-26.2041
179 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1432
Distinct characters7
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-26.2041
2nd row-26.2041
3rd row-26.2041
4th row-26.2041
5th row-26.2041

Common Values

ValueCountFrequency (%)
-26.2041179
100.0%

Length

2025-11-25T00:06:57.525700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:57.564024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
26.2041179
100.0%

Most occurring characters

ValueCountFrequency (%)
2358
25.0%
-179
12.5%
6179
12.5%
.179
12.5%
0179
12.5%
4179
12.5%
1179
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1074
75.0%
Dash Punctuation179
 
12.5%
Other Punctuation179
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2358
33.3%
6179
16.7%
0179
16.7%
4179
16.7%
1179
16.7%
Dash Punctuation
ValueCountFrequency (%)
-179
100.0%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1432
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2358
25.0%
-179
12.5%
6179
12.5%
.179
12.5%
0179
12.5%
4179
12.5%
1179
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1432
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2358
25.0%
-179
12.5%
6179
12.5%
.179
12.5%
0179
12.5%
4179
12.5%
1179
12.5%

longitude
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
28.0473
175 
27.9394
 
4

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1253
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28.0473
2nd row28.0473
3rd row28.0473
4th row28.0473
5th row28.0473

Common Values

ValueCountFrequency (%)
28.0473175
97.8%
27.93944
 
2.2%

Length

2025-11-25T00:06:57.603790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:57.642185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
28.0473175
97.8%
27.93944
 
2.2%

Most occurring characters

ValueCountFrequency (%)
2179
14.3%
.179
14.3%
4179
14.3%
7179
14.3%
3179
14.3%
8175
14.0%
0175
14.0%
98
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1074
85.7%
Other Punctuation179
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2179
16.7%
4179
16.7%
7179
16.7%
3179
16.7%
8175
16.3%
0175
16.3%
98
 
0.7%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1253
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2179
14.3%
.179
14.3%
4179
14.3%
7179
14.3%
3179
14.3%
8175
14.0%
0175
14.0%
98
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1253
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2179
14.3%
.179
14.3%
4179
14.3%
7179
14.3%
3179
14.3%
8175
14.0%
0175
14.0%
98
 
0.6%

jhb_subregion
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
Central_JHB
175 
Western_JHB
 
4

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters1969
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCentral_JHB
2nd rowCentral_JHB
3rd rowCentral_JHB
4th rowCentral_JHB
5th rowCentral_JHB

Common Values

ValueCountFrequency (%)
Central_JHB175
97.8%
Western_JHB4
 
2.2%

Length

2025-11-25T00:06:57.681938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:57.716649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
central_jhb175
97.8%
western_jhb4
 
2.2%

Most occurring characters

ValueCountFrequency (%)
e183
9.3%
n179
9.1%
t179
9.1%
r179
9.1%
_179
9.1%
J179
9.1%
H179
9.1%
B179
9.1%
C175
8.9%
a175
8.9%
Other values (3)183
9.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1074
54.5%
Uppercase Letter716
36.4%
Connector Punctuation179
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e183
17.0%
n179
16.7%
t179
16.7%
r179
16.7%
a175
16.3%
l175
16.3%
s4
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
J179
25.0%
H179
25.0%
B179
25.0%
C175
24.4%
W4
 
0.6%
Connector Punctuation
ValueCountFrequency (%)
_179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1790
90.9%
Common179
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e183
10.2%
n179
10.0%
t179
10.0%
r179
10.0%
J179
10.0%
H179
10.0%
B179
10.0%
C175
9.8%
a175
9.8%
l175
9.8%
Other values (2)8
 
0.4%
Common
ValueCountFrequency (%)
_179
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1969
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e183
9.3%
n179
9.1%
t179
9.1%
r179
9.1%
_179
9.1%
J179
9.1%
H179
9.1%
B179
9.1%
C175
8.9%
a175
8.9%
Other values (3)183
9.3%

city
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
Johannesburg
179 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters2148
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJohannesburg
2nd rowJohannesburg
3rd rowJohannesburg
4th rowJohannesburg
5th rowJohannesburg

Common Values

ValueCountFrequency (%)
Johannesburg179
100.0%

Length

2025-11-25T00:06:57.755768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:57.790585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
johannesburg179
100.0%

Most occurring characters

ValueCountFrequency (%)
n358
16.7%
J179
8.3%
o179
8.3%
h179
8.3%
a179
8.3%
e179
8.3%
s179
8.3%
b179
8.3%
u179
8.3%
r179
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1969
91.7%
Uppercase Letter179
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n358
18.2%
o179
9.1%
h179
9.1%
a179
9.1%
e179
9.1%
s179
9.1%
b179
9.1%
u179
9.1%
r179
9.1%
g179
9.1%
Uppercase Letter
ValueCountFrequency (%)
J179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2148
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n358
16.7%
J179
8.3%
o179
8.3%
h179
8.3%
a179
8.3%
e179
8.3%
s179
8.3%
b179
8.3%
u179
8.3%
r179
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n358
16.7%
J179
8.3%
o179
8.3%
h179
8.3%
a179
8.3%
e179
8.3%
s179
8.3%
b179
8.3%
u179
8.3%
r179
8.3%

province
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
Gauteng
179 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1253
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGauteng
2nd rowGauteng
3rd rowGauteng
4th rowGauteng
5th rowGauteng

Common Values

ValueCountFrequency (%)
Gauteng179
100.0%

Length

2025-11-25T00:06:57.829221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:57.865006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
gauteng179
100.0%

Most occurring characters

ValueCountFrequency (%)
G179
14.3%
a179
14.3%
u179
14.3%
t179
14.3%
e179
14.3%
n179
14.3%
g179
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1074
85.7%
Uppercase Letter179
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a179
16.7%
u179
16.7%
t179
16.7%
e179
16.7%
n179
16.7%
g179
16.7%
Uppercase Letter
ValueCountFrequency (%)
G179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1253
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G179
14.3%
a179
14.3%
u179
14.3%
t179
14.3%
e179
14.3%
n179
14.3%
g179
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1253
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G179
14.3%
a179
14.3%
u179
14.3%
t179
14.3%
e179
14.3%
n179
14.3%
g179
14.3%

country
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
South Africa
179 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters2148
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth Africa
2nd rowSouth Africa
3rd rowSouth Africa
4th rowSouth Africa
5th rowSouth Africa

Common Values

ValueCountFrequency (%)
South Africa179
100.0%

Length

2025-11-25T00:06:57.901935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:57.935044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
south179
50.0%
africa179
50.0%

Most occurring characters

ValueCountFrequency (%)
S179
8.3%
o179
8.3%
u179
8.3%
t179
8.3%
h179
8.3%
179
8.3%
A179
8.3%
f179
8.3%
r179
8.3%
i179
8.3%
Other values (2)358
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1611
75.0%
Uppercase Letter358
 
16.7%
Space Separator179
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o179
11.1%
u179
11.1%
t179
11.1%
h179
11.1%
f179
11.1%
r179
11.1%
i179
11.1%
c179
11.1%
a179
11.1%
Uppercase Letter
ValueCountFrequency (%)
S179
50.0%
A179
50.0%
Space Separator
ValueCountFrequency (%)
179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1969
91.7%
Common179
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
S179
9.1%
o179
9.1%
u179
9.1%
t179
9.1%
h179
9.1%
A179
9.1%
f179
9.1%
r179
9.1%
i179
9.1%
c179
9.1%
Common
ValueCountFrequency (%)
179
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S179
8.3%
o179
8.3%
u179
8.3%
t179
8.3%
h179
8.3%
179
8.3%
A179
8.3%
f179
8.3%
r179
8.3%
i179
8.3%
Other values (2)358
16.7%

date
Date

Distinct86
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum2020-09-08 00:00:00
Maximum2021-07-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T00:06:57.972094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:58.017170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Country
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
South Africa
179 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters2148
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth Africa
2nd rowSouth Africa
3rd rowSouth Africa
4th rowSouth Africa
5th rowSouth Africa

Common Values

ValueCountFrequency (%)
South Africa179
100.0%

Length

2025-11-25T00:06:58.064863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:58.098585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
south179
50.0%
africa179
50.0%

Most occurring characters

ValueCountFrequency (%)
S179
8.3%
o179
8.3%
u179
8.3%
t179
8.3%
h179
8.3%
179
8.3%
A179
8.3%
f179
8.3%
r179
8.3%
i179
8.3%
Other values (2)358
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1611
75.0%
Uppercase Letter358
 
16.7%
Space Separator179
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o179
11.1%
u179
11.1%
t179
11.1%
h179
11.1%
f179
11.1%
r179
11.1%
i179
11.1%
c179
11.1%
a179
11.1%
Uppercase Letter
ValueCountFrequency (%)
S179
50.0%
A179
50.0%
Space Separator
ValueCountFrequency (%)
179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1969
91.7%
Common179
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
S179
9.1%
o179
9.1%
u179
9.1%
t179
9.1%
h179
9.1%
A179
9.1%
f179
9.1%
r179
9.1%
i179
9.1%
c179
9.1%
Common
ValueCountFrequency (%)
179
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S179
8.3%
o179
8.3%
u179
8.3%
t179
8.3%
h179
8.3%
179
8.3%
A179
8.3%
f179
8.3%
r179
8.3%
i179
8.3%
Other values (2)358
16.7%

Clinical Study ID
Categorical

Distinct5
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
Arm B
39 
Arm A
36 
Arm D
36 
Arm C
35 
Arm E
33 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters895
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArm A
2nd rowArm B
3rd rowArm A
4th rowArm C
5th rowArm A

Common Values

ValueCountFrequency (%)
Arm B39
21.8%
Arm A36
20.1%
Arm D36
20.1%
Arm C35
19.6%
Arm E33
18.4%

Length

2025-11-25T00:06:58.134319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:58.172857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
arm179
50.0%
b39
 
10.9%
a36
 
10.1%
d36
 
10.1%
c35
 
9.8%
e33
 
9.2%

Most occurring characters

ValueCountFrequency (%)
A215
24.0%
r179
20.0%
m179
20.0%
179
20.0%
B39
 
4.4%
D36
 
4.0%
C35
 
3.9%
E33
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter358
40.0%
Lowercase Letter358
40.0%
Space Separator179
20.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A215
60.1%
B39
 
10.9%
D36
 
10.1%
C35
 
9.8%
E33
 
9.2%
Lowercase Letter
ValueCountFrequency (%)
r179
50.0%
m179
50.0%
Space Separator
ValueCountFrequency (%)
179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin716
80.0%
Common179
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A215
30.0%
r179
25.0%
m179
25.0%
B39
 
5.4%
D36
 
5.0%
C35
 
4.9%
E33
 
4.6%
Common
ValueCountFrequency (%)
179
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII895
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A215
24.0%
r179
20.0%
m179
20.0%
179
20.0%
B39
 
4.4%
D36
 
4.0%
C35
 
3.9%
E33
 
3.7%

Potassium (mEq/L)
Real number (ℝ)

Serum potassium

Distinct30
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8128492
Minimum3.5
Maximum6.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T00:06:58.214457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile3.9
Q14.4
median4.8
Q35.1
95-th percentile5.9
Maximum6.6
Range3.1
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.60670619
Coefficient of variation (CV)0.12605967
Kurtosis0.32414825
Mean4.8128492
Median Absolute Deviation (MAD)0.4
Skewness0.53010157
Sum861.5
Variance0.3680924
MonotonicityNot monotonic
2025-11-25T00:06:58.256675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4.716
 
8.9%
4.915
 
8.4%
4.815
 
8.4%
4.313
 
7.3%
4.411
 
6.1%
5.110
 
5.6%
4.610
 
5.6%
59
 
5.0%
5.29
 
5.0%
4.18
 
4.5%
Other values (20)63
35.2%
ValueCountFrequency (%)
3.51
 
0.6%
3.61
 
0.6%
3.72
 
1.1%
3.83
 
1.7%
3.96
3.4%
42
 
1.1%
4.18
4.5%
4.26
3.4%
4.313
7.3%
4.411
6.1%
ValueCountFrequency (%)
6.62
 
1.1%
6.41
 
0.6%
6.31
 
0.6%
6.23
1.7%
6.11
 
0.6%
5.93
1.7%
5.82
 
1.1%
5.75
2.8%
5.63
1.7%
5.56
3.4%

respiration rate
Real number (ℝ)

High correlation  Missing 

Distinct8
Distinct (%)16.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean17.94
Minimum14
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T00:06:58.296000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile14.9
Q116
median18
Q319
95-th percentile21
Maximum22
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8888502
Coefficient of variation (CV)0.10528708
Kurtosis-0.05870419
Mean17.94
Median Absolute Deviation (MAD)1.5
Skewness-0.023727412
Sum897
Variance3.5677551
MonotonicityNot monotonic
2025-11-25T00:06:58.332839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1820
 
11.2%
1611
 
6.1%
207
 
3.9%
194
 
2.2%
143
 
1.7%
222
 
1.1%
212
 
1.1%
171
 
0.6%
(Missing)129
72.1%
ValueCountFrequency (%)
143
 
1.7%
1611
6.1%
171
 
0.6%
1820
11.2%
194
 
2.2%
207
 
3.9%
212
 
1.1%
222
 
1.1%
ValueCountFrequency (%)
222
 
1.1%
212
 
1.1%
207
 
3.9%
194
 
2.2%
1820
11.2%
171
 
0.6%
1611
6.1%
143
 
1.7%

Other measures of obesity
Real number (ℝ)

High correlation  Missing 

Distinct50
Distinct (%)100.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean26.533794
Minimum17.50639
Maximum44.88645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T00:06:58.376675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum17.50639
5-th percentile18.452728
Q120.78304
median26.201519
Q331.251109
95-th percentile37.077056
Maximum44.88645
Range27.38006
Interquartile range (IQR)10.468069

Descriptive statistics

Standard deviation6.2062204
Coefficient of variation (CV)0.23389872
Kurtosis0.15607854
Mean26.533794
Median Absolute Deviation (MAD)5.3291005
Skewness0.61774767
Sum1326.6897
Variance38.517172
MonotonicityNot monotonic
2025-11-25T00:06:58.422575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.577037251
 
0.6%
38.285672811
 
0.6%
18.185505681
 
0.6%
33.608396091
 
0.6%
21.254018291
 
0.6%
28.833153061
 
0.6%
25.683116171
 
0.6%
22.21074381
 
0.6%
32.510274321
 
0.6%
31.992171331
 
0.6%
Other values (40)40
 
22.3%
(Missing)129
72.1%
ValueCountFrequency (%)
17.506389831
0.6%
18.185505681
0.6%
18.351020411
0.6%
18.577037251
0.6%
19.34523811
0.6%
19.434400831
0.6%
19.486961451
0.6%
19.829481891
0.6%
20.130457851
0.6%
20.189072261
0.6%
ValueCountFrequency (%)
44.886450241
0.6%
39.10156251
0.6%
38.285672811
0.6%
35.59985761
0.6%
33.608396091
0.6%
32.841490141
0.6%
32.637629721
0.6%
32.510274321
0.6%
32.127362041
0.6%
31.992171331
0.6%

coordinate_source
Categorical

High correlation 

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
JHB_EZIN_025
122 
JHB_VIDA_008
53 
JHB_SCHARP_006
 
4

Length

Max length14
Median length12
Mean length12.044693
Min length12

Characters and Unicode

Total characters2156
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJHB_VIDA_008
2nd rowJHB_VIDA_008
3rd rowJHB_VIDA_008
4th rowJHB_VIDA_008
5th rowJHB_VIDA_008

Common Values

ValueCountFrequency (%)
JHB_EZIN_025122
68.2%
JHB_VIDA_00853
29.6%
JHB_SCHARP_0064
 
2.2%

Length

2025-11-25T00:06:58.472785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:58.512946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
jhb_ezin_025122
68.2%
jhb_vida_00853
29.6%
jhb_scharp_0064
 
2.2%

Most occurring characters

ValueCountFrequency (%)
_358
16.6%
0236
10.9%
H183
8.5%
J179
8.3%
B179
8.3%
I175
8.1%
2122
 
5.7%
5122
 
5.7%
N122
 
5.7%
Z122
 
5.7%
Other values (10)358
16.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1261
58.5%
Decimal Number537
24.9%
Connector Punctuation358
 
16.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H183
14.5%
J179
14.2%
B179
14.2%
I175
13.9%
N122
9.7%
Z122
9.7%
E122
9.7%
A57
 
4.5%
V53
 
4.2%
D53
 
4.2%
Other values (4)16
 
1.3%
Decimal Number
ValueCountFrequency (%)
0236
43.9%
2122
22.7%
5122
22.7%
853
 
9.9%
64
 
0.7%
Connector Punctuation
ValueCountFrequency (%)
_358
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1261
58.5%
Common895
41.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
H183
14.5%
J179
14.2%
B179
14.2%
I175
13.9%
N122
9.7%
Z122
9.7%
E122
9.7%
A57
 
4.5%
V53
 
4.2%
D53
 
4.2%
Other values (4)16
 
1.3%
Common
ValueCountFrequency (%)
_358
40.0%
0236
26.4%
2122
 
13.6%
5122
 
13.6%
853
 
5.9%
64
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2156
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_358
16.6%
0236
10.9%
H183
8.5%
J179
8.3%
B179
8.3%
I175
8.1%
2122
 
5.7%
5122
 
5.7%
N122
 
5.7%
Z122
 
5.7%
Other values (10)358
16.6%

coordinate_precision
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
high
179 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters716
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhigh
2nd rowhigh
3rd rowhigh
4th rowhigh
5th rowhigh

Common Values

ValueCountFrequency (%)
high179
100.0%

Length

2025-11-25T00:06:58.557044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:58.593010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
high179
100.0%

Most occurring characters

ValueCountFrequency (%)
h358
50.0%
i179
25.0%
g179
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter716
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h358
50.0%
i179
25.0%
g179
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin716
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
h358
50.0%
i179
25.0%
g179
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII716
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
h358
50.0%
i179
25.0%
g179
25.0%

geographic_source
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size13.3 KiB
harmonized_datasets
179 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters3401
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowharmonized_datasets
2nd rowharmonized_datasets
3rd rowharmonized_datasets
4th rowharmonized_datasets
5th rowharmonized_datasets

Common Values

ValueCountFrequency (%)
harmonized_datasets179
100.0%

Length

2025-11-25T00:06:58.633052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:58.669732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
harmonized_datasets179
100.0%

Most occurring characters

ValueCountFrequency (%)
a537
15.8%
e358
10.5%
d358
10.5%
t358
10.5%
s358
10.5%
h179
 
5.3%
r179
 
5.3%
m179
 
5.3%
o179
 
5.3%
n179
 
5.3%
Other values (3)537
15.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3222
94.7%
Connector Punctuation179
 
5.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a537
16.7%
e358
11.1%
d358
11.1%
t358
11.1%
s358
11.1%
h179
 
5.6%
r179
 
5.6%
m179
 
5.6%
o179
 
5.6%
n179
 
5.6%
Other values (2)358
11.1%
Connector Punctuation
ValueCountFrequency (%)
_179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3222
94.7%
Common179
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a537
16.7%
e358
11.1%
d358
11.1%
t358
11.1%
s358
11.1%
h179
 
5.6%
r179
 
5.6%
m179
 
5.6%
o179
 
5.6%
n179
 
5.6%
Other values (2)358
11.1%
Common
ValueCountFrequency (%)
_179
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3401
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a537
15.8%
e358
10.5%
d358
10.5%
t358
10.5%
s358
10.5%
h179
 
5.3%
r179
 
5.3%
m179
 
5.3%
o179
 
5.3%
n179
 
5.3%
Other values (3)537
15.8%

HEAT_VULNERABILITY_SCORE
Categorical

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
129 
0.2624535785467304
50 

Length

Max length18
Median length3
Mean length7.1899441
Min length3

Characters and Unicode

Total characters1287
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.2624535785467304
2nd row0.2624535785467304
3rd row0.2624535785467304
4th row0.2624535785467304
5th row0.2624535785467304

Common Values

ValueCountFrequency (%)
0.0129
72.1%
0.262453578546730450
 
27.9%

Length

2025-11-25T00:06:58.707460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:58.745203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0129
72.1%
0.262453578546730450
 
27.9%

Most occurring characters

ValueCountFrequency (%)
0358
27.8%
.179
13.9%
4150
11.7%
5150
11.7%
2100
 
7.8%
6100
 
7.8%
3100
 
7.8%
7100
 
7.8%
850
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1108
86.1%
Other Punctuation179
 
13.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0358
32.3%
4150
13.5%
5150
13.5%
2100
 
9.0%
6100
 
9.0%
3100
 
9.0%
7100
 
9.0%
850
 
4.5%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0358
27.8%
.179
13.9%
4150
11.7%
5150
11.7%
2100
 
7.8%
6100
 
7.8%
3100
 
7.8%
7100
 
7.8%
850
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0358
27.8%
.179
13.9%
4150
11.7%
5150
11.7%
2100
 
7.8%
6100
 
7.8%
3100
 
7.8%
7100
 
7.8%
850
 
3.9%

HEAT_STRESS_RISK_CATEGORY
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
LOW
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters537
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLOW
2nd rowLOW
3rd rowLOW
4th rowLOW
5th rowLOW

Common Values

ValueCountFrequency (%)
LOW179
100.0%

Length

2025-11-25T00:06:58.785055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:58.820651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
low179
100.0%

Most occurring characters

ValueCountFrequency (%)
L179
33.3%
O179
33.3%
W179
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter537
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L179
33.3%
O179
33.3%
W179
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin537
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L179
33.3%
O179
33.3%
W179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L179
33.3%
O179
33.3%
W179
33.3%

HIV_status
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.4 KiB
Positive
179 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1432
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowPositive
3rd rowPositive
4th rowPositive
5th rowPositive

Common Values

ValueCountFrequency (%)
Positive179
100.0%

Length

2025-11-25T00:06:58.859559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:58.897260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
positive179
100.0%

Most occurring characters

ValueCountFrequency (%)
i358
25.0%
P179
12.5%
o179
12.5%
s179
12.5%
t179
12.5%
v179
12.5%
e179
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1253
87.5%
Uppercase Letter179
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i358
28.6%
o179
14.3%
s179
14.3%
t179
14.3%
v179
14.3%
e179
14.3%
Uppercase Letter
ValueCountFrequency (%)
P179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1432
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i358
25.0%
P179
12.5%
o179
12.5%
s179
12.5%
t179
12.5%
v179
12.5%
e179
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1432
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i358
25.0%
P179
12.5%
o179
12.5%
s179
12.5%
t179
12.5%
v179
12.5%
e179
12.5%

johannesburg_metro_valid
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
1.0
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters537
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0179
100.0%

Length

2025-11-25T00:06:58.933732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:58.970034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0179
100.0%

Most occurring characters

ValueCountFrequency (%)
1179
33.3%
.179
33.3%
0179
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number358
66.7%
Other Punctuation179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1179
50.0%
0179
50.0%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1179
33.3%
.179
33.3%
0179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1179
33.3%
.179
33.3%
0179
33.3%

study_site_location
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
Johannesburg (General)
179 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters3938
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJohannesburg (General)
2nd rowJohannesburg (General)
3rd rowJohannesburg (General)
4th rowJohannesburg (General)
5th rowJohannesburg (General)

Common Values

ValueCountFrequency (%)
Johannesburg (General)179
100.0%

Length

2025-11-25T00:06:59.004952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:59.038073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
johannesburg179
50.0%
general179
50.0%

Most occurring characters

ValueCountFrequency (%)
n537
13.6%
e537
13.6%
a358
 
9.1%
r358
 
9.1%
J179
 
4.5%
o179
 
4.5%
h179
 
4.5%
s179
 
4.5%
b179
 
4.5%
u179
 
4.5%
Other values (6)1074
27.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3043
77.3%
Uppercase Letter358
 
9.1%
Space Separator179
 
4.5%
Open Punctuation179
 
4.5%
Close Punctuation179
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n537
17.6%
e537
17.6%
a358
11.8%
r358
11.8%
o179
 
5.9%
h179
 
5.9%
s179
 
5.9%
b179
 
5.9%
u179
 
5.9%
g179
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
J179
50.0%
G179
50.0%
Space Separator
ValueCountFrequency (%)
179
100.0%
Open Punctuation
ValueCountFrequency (%)
(179
100.0%
Close Punctuation
ValueCountFrequency (%)
)179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3401
86.4%
Common537
 
13.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n537
15.8%
e537
15.8%
a358
10.5%
r358
10.5%
J179
 
5.3%
o179
 
5.3%
h179
 
5.3%
s179
 
5.3%
b179
 
5.3%
u179
 
5.3%
Other values (3)537
15.8%
Common
ValueCountFrequency (%)
179
33.3%
(179
33.3%
)179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3938
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n537
13.6%
e537
13.6%
a358
 
9.1%
r358
 
9.1%
J179
 
4.5%
o179
 
4.5%
h179
 
4.5%
s179
 
4.5%
b179
 
4.5%
u179
 
4.5%
Other values (6)1074
27.3%

BMI (kg/m²)
Real number (ℝ)

High correlation  Missing 

Body Mass Index (extreme values removed)

Distinct50
Distinct (%)100.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean26.533794
Minimum17.50639
Maximum44.88645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T00:06:59.076028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum17.50639
5-th percentile18.452728
Q120.78304
median26.201519
Q331.251109
95-th percentile37.077056
Maximum44.88645
Range27.38006
Interquartile range (IQR)10.468069

Descriptive statistics

Standard deviation6.2062204
Coefficient of variation (CV)0.23389872
Kurtosis0.15607854
Mean26.533794
Median Absolute Deviation (MAD)5.3291005
Skewness0.61774767
Sum1326.6897
Variance38.517172
MonotonicityNot monotonic
2025-11-25T00:06:59.121303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.577037251
 
0.6%
38.285672811
 
0.6%
18.185505681
 
0.6%
33.608396091
 
0.6%
21.254018291
 
0.6%
28.833153061
 
0.6%
25.683116171
 
0.6%
22.21074381
 
0.6%
32.510274321
 
0.6%
31.992171331
 
0.6%
Other values (40)40
 
22.3%
(Missing)129
72.1%
ValueCountFrequency (%)
17.506389831
0.6%
18.185505681
0.6%
18.351020411
0.6%
18.577037251
0.6%
19.34523811
0.6%
19.434400831
0.6%
19.486961451
0.6%
19.829481891
0.6%
20.130457851
0.6%
20.189072261
0.6%
ValueCountFrequency (%)
44.886450241
0.6%
39.10156251
0.6%
38.285672811
0.6%
35.59985761
0.6%
33.608396091
0.6%
32.841490141
0.6%
32.637629721
0.6%
32.510274321
0.6%
32.127362041
0.6%
31.992171331
0.6%

Respiratory rate (breaths/min)
Real number (ℝ)

High correlation  Missing 

Respiratory rate

Distinct8
Distinct (%)16.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean17.94
Minimum14
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T00:06:59.161097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile14.9
Q116
median18
Q319
95-th percentile21
Maximum22
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8888502
Coefficient of variation (CV)0.10528708
Kurtosis-0.05870419
Mean17.94
Median Absolute Deviation (MAD)1.5
Skewness-0.023727412
Sum897
Variance3.5677551
MonotonicityNot monotonic
2025-11-25T00:06:59.198284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1820
 
11.2%
1611
 
6.1%
207
 
3.9%
194
 
2.2%
143
 
1.7%
222
 
1.1%
212
 
1.1%
171
 
0.6%
(Missing)129
72.1%
ValueCountFrequency (%)
143
 
1.7%
1611
6.1%
171
 
0.6%
1820
11.2%
194
 
2.2%
207
 
3.9%
212
 
1.1%
222
 
1.1%
ValueCountFrequency (%)
222
 
1.1%
212
 
1.1%
207
 
3.9%
194
 
2.2%
1820
11.2%
171
 
0.6%
1611
6.1%
143
 
1.7%

climate_daily_mean_temp
Real number (ℝ)

High correlation 

Daily mean temperature

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.188184
Minimum7.098
Maximum21.626
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T00:06:59.233593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.098
5-th percentile7.098
Q17.098
median13.599
Q319.07
95-th percentile21.626
Maximum21.626
Range14.528
Interquartile range (IQR)11.972

Descriptive statistics

Standard deviation5.4715296
Coefficient of variation (CV)0.38563987
Kurtosis-1.5230969
Mean14.188184
Median Absolute Deviation (MAD)5.471
Skewness-0.083238863
Sum2539.685
Variance29.937636
MonotonicityNot monotonic
2025-11-25T00:06:59.270325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7.09850
27.9%
11.38131
17.3%
21.62629
16.2%
19.0715
 
8.4%
18.28512
 
6.7%
16.65312
 
6.7%
13.59910
 
5.6%
17.6669
 
5.0%
18.3525
 
2.8%
19.3315
 
2.8%
ValueCountFrequency (%)
7.09850
27.9%
11.38131
17.3%
13.59910
 
5.6%
16.1151
 
0.6%
16.65312
 
6.7%
17.6669
 
5.0%
18.28512
 
6.7%
18.3525
 
2.8%
19.0715
 
8.4%
19.3315
 
2.8%
ValueCountFrequency (%)
21.62629
16.2%
19.3315
 
2.8%
19.0715
8.4%
18.3525
 
2.8%
18.28512
 
6.7%
17.6669
 
5.0%
16.65312
 
6.7%
16.1151
 
0.6%
13.59910
 
5.6%
11.38131
17.3%

climate_daily_max_temp
Real number (ℝ)

High correlation 

Daily maximum temperature

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.255441
Minimum13.147
Maximum26.902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T00:06:59.305602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum13.147
5-th percentile13.147
Q113.147
median19.978
Q325.656
95-th percentile26.902
Maximum26.902
Range13.755
Interquartile range (IQR)12.509

Descriptive statistics

Standard deviation5.2852604
Coefficient of variation (CV)0.2609304
Kurtosis-1.4871955
Mean20.255441
Median Absolute Deviation (MAD)5.678
Skewness-0.18123378
Sum3625.724
Variance27.933978
MonotonicityNot monotonic
2025-11-25T00:06:59.340010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
13.14750
27.9%
18.231
17.3%
26.90229
16.2%
24.3615
 
8.4%
25.65612
 
6.7%
21.60712
 
6.7%
19.97810
 
5.6%
25.9319
 
5.0%
23.4215
 
2.8%
23.8895
 
2.8%
ValueCountFrequency (%)
13.14750
27.9%
18.231
17.3%
19.97810
 
5.6%
21.60712
 
6.7%
21.7511
 
0.6%
23.4215
 
2.8%
23.8895
 
2.8%
24.3615
 
8.4%
25.65612
 
6.7%
25.9319
 
5.0%
ValueCountFrequency (%)
26.90229
16.2%
25.9319
 
5.0%
25.65612
 
6.7%
24.3615
8.4%
23.8895
 
2.8%
23.4215
 
2.8%
21.7511
 
0.6%
21.60712
 
6.7%
19.97810
 
5.6%
18.231
17.3%

climate_daily_min_temp
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2291899
Minimum1.468
Maximum16.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T00:06:59.374721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.468
5-th percentile1.468
Q11.468
median7.473
Q314.512
95-th percentile16.62
Maximum16.62
Range15.152
Interquartile range (IQR)13.044

Descriptive statistics

Standard deviation5.6820579
Coefficient of variation (CV)0.69047597
Kurtosis-1.4289073
Mean8.2291899
Median Absolute Deviation (MAD)6.005
Skewness0.21403409
Sum1473.025
Variance32.285782
MonotonicityNot monotonic
2025-11-25T00:06:59.410420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1.46850
27.9%
4.96431
17.3%
16.6229
16.2%
14.51215
 
8.4%
10.03812
 
6.7%
10.95212
 
6.7%
7.47310
 
5.6%
7.6469
 
5.0%
12.6975
 
2.8%
15.3835
 
2.8%
ValueCountFrequency (%)
1.46850
27.9%
4.96431
17.3%
7.47310
 
5.6%
7.6469
 
5.0%
10.03812
 
6.7%
10.2571
 
0.6%
10.95212
 
6.7%
12.6975
 
2.8%
14.51215
 
8.4%
15.3835
 
2.8%
ValueCountFrequency (%)
16.6229
16.2%
15.3835
 
2.8%
14.51215
8.4%
12.6975
 
2.8%
10.95212
 
6.7%
10.2571
 
0.6%
10.03812
 
6.7%
7.6469
 
5.0%
7.47310
 
5.6%
4.96431
17.3%

climate_7d_mean_temp
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.505022
Minimum8.244
Maximum20.221
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T00:06:59.535395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8.244
5-th percentile8.244
Q18.244
median13.294
Q318.832
95-th percentile19.788
Maximum20.221
Range11.977
Interquartile range (IQR)10.588

Descriptive statistics

Standard deviation4.5823236
Coefficient of variation (CV)0.3159129
Kurtosis-1.5240013
Mean14.505022
Median Absolute Deviation (MAD)5.05
Skewness-0.26204287
Sum2596.399
Variance20.99769
MonotonicityNot monotonic
2025-11-25T00:06:59.574601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8.24450
27.9%
12.94631
17.3%
19.78829
16.2%
18.83215
 
8.4%
18.60212
 
6.7%
17.59912
 
6.7%
13.29410
 
5.6%
16.8239
 
5.0%
18.1875
 
2.8%
20.2215
 
2.8%
ValueCountFrequency (%)
8.24450
27.9%
12.94631
17.3%
13.29410
 
5.6%
15.7421
 
0.6%
16.8239
 
5.0%
17.59912
 
6.7%
18.1875
 
2.8%
18.60212
 
6.7%
18.83215
 
8.4%
19.78829
16.2%
ValueCountFrequency (%)
20.2215
 
2.8%
19.78829
16.2%
18.83215
8.4%
18.60212
 
6.7%
18.1875
 
2.8%
17.59912
 
6.7%
16.8239
 
5.0%
15.7421
 
0.6%
13.29410
 
5.6%
12.94631
17.3%

climate_7d_max_temp
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.690832
Minimum18.344
Maximum31.094
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T00:06:59.609834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18.344
5-th percentile18.344
Q118.344
median22.614
Q327.183
95-th percentile31.094
Maximum31.094
Range12.75
Interquartile range (IQR)8.839

Descriptive statistics

Standard deviation4.1141998
Coefficient of variation (CV)0.17366211
Kurtosis-1.2017703
Mean23.690832
Median Absolute Deviation (MAD)4.27
Skewness0.029480078
Sum4240.659
Variance16.92664
MonotonicityNot monotonic
2025-11-25T00:06:59.649339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
18.34450
27.9%
22.61431
17.3%
27.18329
16.2%
24.97815
 
8.4%
31.09412
 
6.7%
26.4512
 
6.7%
21.40510
 
5.6%
26.729
 
5.0%
28.845
 
2.8%
29.2125
 
2.8%
ValueCountFrequency (%)
18.34450
27.9%
21.40510
 
5.6%
22.61431
17.3%
24.131
 
0.6%
24.97815
 
8.4%
26.4512
 
6.7%
26.729
 
5.0%
27.18329
16.2%
28.845
 
2.8%
29.2125
 
2.8%
ValueCountFrequency (%)
31.09412
 
6.7%
29.2125
 
2.8%
28.845
 
2.8%
27.18329
16.2%
26.729
 
5.0%
26.4512
 
6.7%
24.97815
8.4%
24.131
 
0.6%
22.61431
17.3%
21.40510
 
5.6%

climate_14d_mean_temp
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.867106
Minimum9.443
Maximum20.751
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T00:06:59.685304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum9.443
5-th percentile9.443
Q19.443
median14.097
Q318.834
95-th percentile20.751
Maximum20.751
Range11.308
Interquartile range (IQR)9.391

Descriptive statistics

Standard deviation4.4449059
Coefficient of variation (CV)0.29897586
Kurtosis-1.6672585
Mean14.867106
Median Absolute Deviation (MAD)4.654
Skewness0.0083155513
Sum2661.212
Variance19.757189
MonotonicityNot monotonic
2025-11-25T00:06:59.721117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
9.44350
27.9%
11.88331
17.3%
20.75129
16.2%
18.55615
 
8.4%
18.83412
 
6.7%
18.02812
 
6.7%
14.09710
 
5.6%
16.0119
 
5.0%
19.9665
 
2.8%
19.4095
 
2.8%
ValueCountFrequency (%)
9.44350
27.9%
11.88331
17.3%
14.09710
 
5.6%
16.0119
 
5.0%
16.2821
 
0.6%
18.02812
 
6.7%
18.55615
 
8.4%
18.83412
 
6.7%
19.4095
 
2.8%
19.9665
 
2.8%
ValueCountFrequency (%)
20.75129
16.2%
19.9665
 
2.8%
19.4095
 
2.8%
18.83412
 
6.7%
18.55615
8.4%
18.02812
 
6.7%
16.2821
 
0.6%
16.0119
 
5.0%
14.09710
 
5.6%
11.88331
17.3%

climate_30d_mean_temp
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.231067
Minimum10.613
Maximum20.855
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T00:06:59.755429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10.613
5-th percentile10.613
Q110.613
median14.519
Q319.102
95-th percentile20.855
Maximum20.855
Range10.242
Interquartile range (IQR)8.489

Descriptive statistics

Standard deviation4.3318452
Coefficient of variation (CV)0.28440852
Kurtosis-1.8128068
Mean15.231067
Median Absolute Deviation (MAD)3.906
Skewness0.071129169
Sum2726.361
Variance18.764883
MonotonicityNot monotonic
2025-11-25T00:06:59.789925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10.61350
27.9%
10.97831
17.3%
20.85529
16.2%
18.81515
 
8.4%
19.10212
 
6.7%
18.94212
 
6.7%
14.51910
 
5.6%
16.6769
 
5.0%
20.5285
 
2.8%
19.5445
 
2.8%
ValueCountFrequency (%)
10.61350
27.9%
10.97831
17.3%
14.51910
 
5.6%
16.2111
 
0.6%
16.6769
 
5.0%
18.81515
 
8.4%
18.94212
 
6.7%
19.10212
 
6.7%
19.5445
 
2.8%
20.5285
 
2.8%
ValueCountFrequency (%)
20.85529
16.2%
20.5285
 
2.8%
19.5445
 
2.8%
19.10212
 
6.7%
18.94212
 
6.7%
18.81515
8.4%
16.6769
 
5.0%
16.2111
 
0.6%
14.51910
 
5.6%
10.97831
17.3%

climate_temp_anomaly
Real number (ℝ)

High correlation 

Temperature anomaly from baseline

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0242346
Minimum2.534
Maximum9.255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T00:06:59.821329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.534
5-th percentile2.534
Q12.534
median5.544
Q36.554
95-th percentile7.4253
Maximum9.255
Range6.721
Interquartile range (IQR)4.02

Descriptive statistics

Standard deviation2.0897289
Coefficient of variation (CV)0.4159298
Kurtosis-1.1912532
Mean5.0242346
Median Absolute Deviation (MAD)1.678
Skewness0.067239824
Sum899.338
Variance4.3669669
MonotonicityNot monotonic
2025-11-25T00:06:59.857064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2.53450
27.9%
7.22231
17.3%
6.04729
16.2%
5.54415
 
8.4%
6.55412
 
6.7%
2.66512
 
6.7%
5.45810
 
5.6%
9.2559
 
5.0%
2.8935
 
2.8%
4.3455
 
2.8%
ValueCountFrequency (%)
2.53450
27.9%
2.66512
 
6.7%
2.8935
 
2.8%
4.3455
 
2.8%
5.45810
 
5.6%
5.541
 
0.6%
5.54415
 
8.4%
6.04729
16.2%
6.55412
 
6.7%
7.22231
17.3%
ValueCountFrequency (%)
9.2559
 
5.0%
7.22231
17.3%
6.55412
 
6.7%
6.04729
16.2%
5.54415
8.4%
5.541
 
0.6%
5.45810
 
5.6%
4.3455
 
2.8%
2.8935
 
2.8%
2.66512
 
6.7%

climate_standardized_anomaly
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.20086034
Minimum-1.246
Maximum0.843
Zeros0
Zeros (%)0.0%
Negative99
Negative (%)55.3%
Memory size2.8 KiB
2025-11-25T00:06:59.893596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1.246
5-th percentile-1.246
Q1-1.246
median-0.204
Q30.671
95-th percentile0.843
Maximum0.843
Range2.089
Interquartile range (IQR)1.917

Descriptive statistics

Standard deviation0.82121833
Coefficient of variation (CV)-4.0885042
Kurtosis-1.5735704
Mean-0.20086034
Median Absolute Deviation (MAD)0.875
Skewness-0.06388634
Sum-35.954
Variance0.67439955
MonotonicityNot monotonic
2025-11-25T00:06:59.928285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
-1.24650
27.9%
0.67131
17.3%
0.84329
16.2%
-0.43915
 
8.4%
-0.20412
 
6.7%
-0.89812
 
6.7%
0.3110
 
5.6%
0.0649
 
5.0%
-0.3365
 
2.8%
-0.2725
 
2.8%
ValueCountFrequency (%)
-1.24650
27.9%
-0.89812
 
6.7%
-0.43915
 
8.4%
-0.3365
 
2.8%
-0.2725
 
2.8%
-0.20412
 
6.7%
0.0649
 
5.0%
0.2711
 
0.6%
0.3110
 
5.6%
0.67131
17.3%
ValueCountFrequency (%)
0.84329
16.2%
0.67131
17.3%
0.3110
 
5.6%
0.2711
 
0.6%
0.0649
 
5.0%
-0.20412
 
6.7%
-0.2725
 
2.8%
-0.3365
 
2.8%
-0.43915
8.4%
-0.89812
 
6.7%

climate_heat_day_p90
Categorical

Constant 

Heat day indicator (>90th percentile)

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0.0
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters537
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0179
100.0%

Length

2025-11-25T00:06:59.970005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:07:00.004966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0179
100.0%

Most occurring characters

ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number358
66.7%
Other Punctuation179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0358
100.0%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

climate_heat_day_p95
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0.0
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters537
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0179
100.0%

Length

2025-11-25T00:07:00.044010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:07:00.079384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0179
100.0%

Most occurring characters

ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number358
66.7%
Other Punctuation179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0358
100.0%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

climate_heat_stress_index
Real number (ℝ)

High correlation 

Heat stress index

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.58362
Minimum7.393
Maximum22.548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T00:07:00.109681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.393
5-th percentile7.393
Q17.393
median16.134
Q320.129
95-th percentile20.5932
Maximum22.548
Range15.155
Interquartile range (IQR)12.736

Descriptive statistics

Standard deviation5.5039646
Coefficient of variation (CV)0.37740729
Kurtosis-1.6187301
Mean14.58362
Median Absolute Deviation (MAD)4.811
Skewness-0.17802262
Sum2610.468
Variance30.293626
MonotonicityNot monotonic
2025-11-25T00:07:00.145382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7.39350
27.9%
11.32331
17.3%
20.12929
16.2%
20.37615
 
8.4%
19.34312
 
6.7%
18.07812
 
6.7%
16.13410
 
5.6%
22.5489
 
5.0%
17.4245
 
2.8%
16.3575
 
2.8%
ValueCountFrequency (%)
7.39350
27.9%
11.32331
17.3%
16.13410
 
5.6%
16.3575
 
2.8%
17.4245
 
2.8%
18.07812
 
6.7%
18.1951
 
0.6%
19.34312
 
6.7%
20.12929
16.2%
20.37615
 
8.4%
ValueCountFrequency (%)
22.5489
 
5.0%
20.37615
8.4%
20.12929
16.2%
19.34312
 
6.7%
18.1951
 
0.6%
18.07812
 
6.7%
17.4245
 
2.8%
16.3575
 
2.8%
16.13410
 
5.6%
11.32331
17.3%

climate_p90_threshold
Categorical

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
28.409
129 
28.246
50 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1074
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28.409
2nd row28.409
3rd row28.409
4th row28.409
5th row28.409

Common Values

ValueCountFrequency (%)
28.409129
72.1%
28.24650
 
27.9%

Length

2025-11-25T00:07:00.189153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:07:00.225313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
28.409129
72.1%
28.24650
 
27.9%

Most occurring characters

ValueCountFrequency (%)
2229
21.3%
8179
16.7%
.179
16.7%
4179
16.7%
0129
12.0%
9129
12.0%
650
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number895
83.3%
Other Punctuation179
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2229
25.6%
8179
20.0%
4179
20.0%
0129
14.4%
9129
14.4%
650
 
5.6%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1074
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2229
21.3%
8179
16.7%
.179
16.7%
4179
16.7%
0129
12.0%
9129
12.0%
650
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2229
21.3%
8179
16.7%
.179
16.7%
4179
16.7%
0129
12.0%
9129
12.0%
650
 
4.7%

climate_p95_threshold
Categorical

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
29.704
129 
29.513
50 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1074
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29.704
2nd row29.704
3rd row29.704
4th row29.704
5th row29.704

Common Values

ValueCountFrequency (%)
29.704129
72.1%
29.51350
 
27.9%

Length

2025-11-25T00:07:00.265547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:07:00.302905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
29.704129
72.1%
29.51350
 
27.9%

Most occurring characters

ValueCountFrequency (%)
2179
16.7%
9179
16.7%
.179
16.7%
7129
12.0%
0129
12.0%
4129
12.0%
550
 
4.7%
150
 
4.7%
350
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number895
83.3%
Other Punctuation179
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2179
20.0%
9179
20.0%
7129
14.4%
0129
14.4%
4129
14.4%
550
 
5.6%
150
 
5.6%
350
 
5.6%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1074
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2179
16.7%
9179
16.7%
.179
16.7%
7129
12.0%
0129
12.0%
4129
12.0%
550
 
4.7%
150
 
4.7%
350
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2179
16.7%
9179
16.7%
.179
16.7%
7129
12.0%
0129
12.0%
4129
12.0%
550
 
4.7%
150
 
4.7%
350
 
4.7%

climate_p99_threshold
Categorical

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
31.797
129 
31.748
50 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1074
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row31.797
2nd row31.797
3rd row31.797
4th row31.797
5th row31.797

Common Values

ValueCountFrequency (%)
31.797129
72.1%
31.74850
 
27.9%

Length

2025-11-25T00:07:00.342083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:07:00.380318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
31.797129
72.1%
31.74850
 
27.9%

Most occurring characters

ValueCountFrequency (%)
7308
28.7%
3179
16.7%
1179
16.7%
.179
16.7%
9129
12.0%
450
 
4.7%
850
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number895
83.3%
Other Punctuation179
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7308
34.4%
3179
20.0%
1179
20.0%
9129
14.4%
450
 
5.6%
850
 
5.6%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1074
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7308
28.7%
3179
16.7%
1179
16.7%
.179
16.7%
9129
12.0%
450
 
4.7%
850
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7308
28.7%
3179
16.7%
1179
16.7%
.179
16.7%
9129
12.0%
450
 
4.7%
850
 
4.7%

climate_season
Categorical

High correlation 

Distinct4
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
Winter
81 
Summer
49 
Spring
26 
Autumn
23 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1074
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpring
2nd rowSpring
3rd rowSpring
4th rowSpring
5th rowSpring

Common Values

ValueCountFrequency (%)
Winter81
45.3%
Summer49
27.4%
Spring26
 
14.5%
Autumn23
 
12.8%

Length

2025-11-25T00:07:00.418974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:07:00.458471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
winter81
45.3%
summer49
27.4%
spring26
 
14.5%
autumn23
 
12.8%

Most occurring characters

ValueCountFrequency (%)
r156
14.5%
n130
12.1%
e130
12.1%
m121
11.3%
i107
10.0%
t104
9.7%
u95
8.8%
W81
7.5%
S75
7.0%
p26
 
2.4%
Other values (2)49
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter895
83.3%
Uppercase Letter179
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r156
17.4%
n130
14.5%
e130
14.5%
m121
13.5%
i107
12.0%
t104
11.6%
u95
10.6%
p26
 
2.9%
g26
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
W81
45.3%
S75
41.9%
A23
 
12.8%

Most occurring scripts

ValueCountFrequency (%)
Latin1074
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r156
14.5%
n130
12.1%
e130
12.1%
m121
11.3%
i107
10.0%
t104
9.7%
u95
8.8%
W81
7.5%
S75
7.0%
p26
 
2.4%
Other values (2)49
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r156
14.5%
n130
12.1%
e130
12.1%
m121
11.3%
i107
10.0%
t104
9.7%
u95
8.8%
W81
7.5%
S75
7.0%
p26
 
2.4%
Other values (2)49
 
4.6%

heart_rate_bpm
Real number (ℝ)

High correlation  Missing 

Heart rate (zero values removed)

Distinct34
Distinct (%)68.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean78
Minimum50
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T00:07:00.500907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile55.45
Q170.25
median76
Q386
95-th percentile102.2
Maximum110
Range60
Interquartile range (IQR)15.75

Descriptive statistics

Standard deviation13.810673
Coefficient of variation (CV)0.17705991
Kurtosis-0.15961177
Mean78
Median Absolute Deviation (MAD)9
Skewness0.20889091
Sum3900
Variance190.73469
MonotonicityNot monotonic
2025-11-25T00:07:00.543323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
744
 
2.2%
673
 
1.7%
723
 
1.7%
832
 
1.1%
762
 
1.1%
862
 
1.1%
732
 
1.1%
822
 
1.1%
802
 
1.1%
642
 
1.1%
Other values (24)26
 
14.5%
(Missing)129
72.1%
ValueCountFrequency (%)
501
 
0.6%
511
 
0.6%
551
 
0.6%
561
 
0.6%
601
 
0.6%
631
 
0.6%
642
1.1%
661
 
0.6%
673
1.7%
701
 
0.6%
ValueCountFrequency (%)
1101
0.6%
1051
0.6%
1041
0.6%
1001
0.6%
991
0.6%
962
1.1%
941
0.6%
911
0.6%
891
0.6%
881
0.6%

body_temperature_celsius
Real number (ℝ)

High correlation  Missing 

Body temperature

Distinct17
Distinct (%)34.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean36.488
Minimum35.2
Maximum37.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T00:07:00.581376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum35.2
5-th percentile35.9
Q136.2
median36.45
Q336.7
95-th percentile37.265
Maximum37.7
Range2.5
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.46582821
Coefficient of variation (CV)0.012766614
Kurtosis0.904937
Mean36.488
Median Absolute Deviation (MAD)0.25
Skewness0.028784237
Sum1824.4
Variance0.21699592
MonotonicityNot monotonic
2025-11-25T00:07:00.619435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
36.47
 
3.9%
36.77
 
3.9%
36.55
 
2.8%
36.34
 
2.2%
364
 
2.2%
36.13
 
1.7%
36.83
 
1.7%
36.23
 
1.7%
373
 
1.7%
37.42
 
1.1%
Other values (7)9
 
5.0%
(Missing)129
72.1%
ValueCountFrequency (%)
35.21
 
0.6%
35.51
 
0.6%
35.92
 
1.1%
364
2.2%
36.13
1.7%
36.23
1.7%
36.34
2.2%
36.47
3.9%
36.55
2.8%
36.61
 
0.6%
ValueCountFrequency (%)
37.71
 
0.6%
37.42
 
1.1%
37.12
 
1.1%
373
1.7%
36.91
 
0.6%
36.83
1.7%
36.77
3.9%
36.61
 
0.6%
36.55
2.8%
36.47
3.9%

weight_kg
Real number (ℝ)

High correlation  Missing 

Body weight in kilograms

Distinct47
Distinct (%)94.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean74.736
Minimum49.9
Maximum117.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T00:07:00.661289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum49.9
5-th percentile50.85
Q160.55
median73.1
Q384.9
95-th percentile107.7
Maximum117.8
Range67.9
Interquartile range (IQR)24.35

Descriptive statistics

Standard deviation17.108761
Coefficient of variation (CV)0.22892262
Kurtosis-0.14569641
Mean74.736
Median Absolute Deviation (MAD)12.05
Skewness0.57554181
Sum3736.8
Variance292.7097
MonotonicityNot monotonic
2025-11-25T00:07:00.708090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
902
 
1.1%
70.92
 
1.1%
73.12
 
1.1%
58.21
 
0.6%
83.91
 
0.6%
68.11
 
0.6%
85.31
 
0.6%
75.11
 
0.6%
68.81
 
0.6%
97.31
 
0.6%
Other values (37)37
 
20.7%
(Missing)129
72.1%
ValueCountFrequency (%)
49.91
0.6%
501
0.6%
50.41
0.6%
51.41
0.6%
53.81
0.6%
54.51
0.6%
54.61
0.6%
551
0.6%
56.21
0.6%
58.21
0.6%
ValueCountFrequency (%)
117.81
0.6%
112.41
0.6%
109.51
0.6%
105.51
0.6%
100.11
0.6%
97.31
0.6%
91.91
0.6%
902
1.1%
89.61
0.6%
88.41
0.6%

height_m
Real number (ℝ)

High correlation  Missing 

Height in meters

Distinct26
Distinct (%)52.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean1.681
Minimum1.52
Maximum1.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T00:07:00.750670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.52
5-th percentile1.559
Q11.62
median1.68
Q31.75
95-th percentile1.79
Maximum1.87
Range0.35
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.080311892
Coefficient of variation (CV)0.047776259
Kurtosis-0.52690869
Mean1.681
Median Absolute Deviation (MAD)0.07
Skewness0.19250412
Sum84.05
Variance0.00645
MonotonicityNot monotonic
2025-11-25T00:07:00.790995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1.754
 
2.2%
1.614
 
2.2%
1.724
 
2.2%
1.793
 
1.7%
1.643
 
1.7%
1.653
 
1.7%
1.683
 
1.7%
1.762
 
1.1%
1.632
 
1.1%
1.772
 
1.1%
Other values (16)20
 
11.2%
(Missing)129
72.1%
ValueCountFrequency (%)
1.521
 
0.6%
1.552
1.1%
1.571
 
0.6%
1.582
1.1%
1.591
 
0.6%
1.61
 
0.6%
1.614
2.2%
1.622
1.1%
1.632
1.1%
1.643
1.7%
ValueCountFrequency (%)
1.871
 
0.6%
1.851
 
0.6%
1.793
1.7%
1.781
 
0.6%
1.772
1.1%
1.762
1.1%
1.754
2.2%
1.731
 
0.6%
1.724
2.2%
1.711
 
0.6%

sa_biomarker_standards
Categorical

Constant 

South African biomarker reference standards

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
1.0
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters537
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0179
100.0%

Length

2025-11-25T00:07:00.836031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:07:00.871701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0179
100.0%

Most occurring characters

ValueCountFrequency (%)
1179
33.3%
.179
33.3%
0179
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number358
66.7%
Other Punctuation179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1179
50.0%
0179
50.0%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1179
33.3%
.179
33.3%
0179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1179
33.3%
.179
33.3%
0179
33.3%

cd4_correction_applied
Categorical

Constant 

Quality flag: CD4 missing codes removed

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0.0
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters537
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0179
100.0%

Length

2025-11-25T00:07:00.909940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:07:00.944805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0179
100.0%

Most occurring characters

ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number358
66.7%
Other Punctuation179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0358
100.0%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

final_comprehensive_fix_applied
Categorical

Constant 

Quality flag: Comprehensive corrections applied

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
1.0
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters537
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0179
100.0%

Length

2025-11-25T00:07:00.981962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:07:01.016473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0179
100.0%

Most occurring characters

ValueCountFrequency (%)
1179
33.3%
.179
33.3%
0179
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number358
66.7%
Other Punctuation179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1179
50.0%
0179
50.0%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1179
33.3%
.179
33.3%
0179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1179
33.3%
.179
33.3%
0179
33.3%

total_protein_extreme_flag
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0.0
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters537
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0179
100.0%

Length

2025-11-25T00:07:01.053612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:07:01.089885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0179
100.0%

Most occurring characters

ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number358
66.7%
Other Punctuation179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0358
100.0%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0358
66.7%
.179
33.3%
Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0.0
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters537
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0179
100.0%

Length

2025-11-25T00:07:01.126192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:07:01.162027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0179
100.0%

Most occurring characters

ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number358
66.7%
Other Punctuation179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0358
100.0%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0358
66.7%
.179
33.3%
Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0.0
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters537
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0179
100.0%

Length

2025-11-25T00:07:01.199457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:07:01.233736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0179
100.0%

Most occurring characters

ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number358
66.7%
Other Punctuation179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0358
100.0%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

quality_harmonization_version
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
2.0
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters537
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0179
100.0%

Length

2025-11-25T00:07:01.271095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:07:01.306258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0179
100.0%

Most occurring characters

ValueCountFrequency (%)
2179
33.3%
.179
33.3%
0179
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number358
66.7%
Other Punctuation179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2179
50.0%
0179
50.0%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2179
33.3%
.179
33.3%
0179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2179
33.3%
.179
33.3%
0179
33.3%

waist_circ_unit_correction_applied
Boolean

Constant 

Quality flag: Waist circ unit corrected

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
179 
ValueCountFrequency (%)
False179
100.0%
2025-11-25T00:07:01.337135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Interactions

2025-11-25T00:06:56.063319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:44.059160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:44.710392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:45.406037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:46.020237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:46.616768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:47.200002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:47.919561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:48.556889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:49.176339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:49.858492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:50.465589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:51.063683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:51.740286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:52.320125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:52.922122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:53.603259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:54.210957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:54.801419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:55.378013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:56.090268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:44.086195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:44.738769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:45.435761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:46.048831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:46.644591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:47.230481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:47.949905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:48.585507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:49.205358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:49.886734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:50.493662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:51.094040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:51.768652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:52.347633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:52.949549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:53.632413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:54.240152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:54.827812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:55.407610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:56.120449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:44.128763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:44.770663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:45.465452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:46.077308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:46.673311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:47.260562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:47.981465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:48.616403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:49.238001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:49.917768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:50.525513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:51.124362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:51.797768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:52.379730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:52.978340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:53.664610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:54.268712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:54.856338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:55.436317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:56.152576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:44.177270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:44.801824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:45.498634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:46.110254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:46.704968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:47.386998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:48.016778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:48.650166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:49.356461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:49.949444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:50.555596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:51.156443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:51.827805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:52.411545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:53.011390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:53.695295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:54.299948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:54.887464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:55.469124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:56.182576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:44.204918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:44.830615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:45.528171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:46.138493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:46.734456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:47.416591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:48.046914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:48.679615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:49.383756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:49.980364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:50.583364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:51.183875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:51.855619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:52.439497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:53.040326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:53.724125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:54.329263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:54.916534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:55.498044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:56.210291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:44.230400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:44.858842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:45.558671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:46.165696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:46.761705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:47.447817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:48.078312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:48.711034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:49.411366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:50.008873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:50.610291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:51.211690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:51.882296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:52.468554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:53.070395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-11-25T00:06:55.913565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-11-25T00:06:44.596089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-11-25T00:06:56.001752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:56.625075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:44.680292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:45.377453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:45.988259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:46.585519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:47.170256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:47.887965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:48.523308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:49.143627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:49.828511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:50.434683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:51.034607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:51.711922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:52.290649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-11-25T00:06:56.031484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-11-25T00:07:01.373561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
BMI (kg/m²)Clinical Study IDHEAT_VULNERABILITY_SCOREOther measures of obesityPotassium (mEq/L)Respiratory rate (breaths/min)body_temperature_celsiusclimate_14d_mean_tempclimate_30d_mean_tempclimate_7d_max_tempclimate_7d_mean_tempclimate_daily_max_tempclimate_daily_mean_tempclimate_daily_min_tempclimate_heat_stress_indexclimate_p90_thresholdclimate_p95_thresholdclimate_p99_thresholdclimate_seasonclimate_standardized_anomalyclimate_temp_anomalycoordinate_sourceheart_rate_bpmheight_mjhb_subregionlongitudemonthrespiration rateseasonweight_kgyear
BMI (kg/m²)1.0000.0001.0001.0000.1170.005-0.1260.1510.151-0.2230.1020.2560.1590.1590.2021.0001.0001.0000.0000.256-0.0050.0000.187-0.2981.0001.000-0.2480.0050.0000.9100.000
Clinical Study ID0.0001.0000.0000.0000.1090.0000.0940.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0620.000
HEAT_VULNERABILITY_SCORE1.0000.0001.0001.0000.1281.0001.0000.7390.6850.7890.6390.8650.8270.8590.6350.3670.3670.3670.8040.7810.7980.7091.0001.0000.0000.0000.8121.0000.8041.0000.717
Other measures of obesity1.0000.0001.0001.0000.1170.005-0.1260.1510.151-0.2230.1020.2560.1590.1590.2021.0001.0001.0000.0000.256-0.0050.0000.187-0.2981.0001.000-0.2480.0050.0000.9100.000
Potassium (mEq/L)0.1170.1090.1280.1171.000-0.020-0.268-0.211-0.216-0.251-0.190-0.239-0.204-0.192-0.2430.3260.3260.3260.132-0.187-0.2470.000-0.2750.1600.0000.000-0.055-0.0200.1320.1820.058
Respiratory rate (breaths/min)0.0050.0001.0000.005-0.0201.0000.3270.3060.3060.0690.3120.0720.3180.318-0.1511.0001.0001.0000.1690.072-0.3000.000-0.020-0.0141.0001.000-0.0591.0000.1690.0080.114
body_temperature_celsius-0.1260.0941.000-0.126-0.2680.3271.0000.3150.315-0.1440.3010.3100.3370.3370.1111.0001.0001.0000.2180.310-0.1400.0000.294-0.0971.0001.000-0.2880.3270.218-0.1420.253
climate_14d_mean_temp0.1510.0000.7390.151-0.2110.3060.3151.0000.9950.9330.9810.9600.9870.9810.8640.9830.9830.9830.8620.6900.4730.482-0.0470.0340.0000.000-0.3160.3060.8620.2040.697
climate_30d_mean_temp0.1510.0000.6850.151-0.2160.3060.3150.9951.0000.9380.9700.9510.9720.9730.8430.6700.6700.6700.7520.6850.4620.474-0.0470.0340.0720.072-0.3090.3060.7520.2040.609
climate_7d_max_temp-0.2230.0000.789-0.223-0.2510.069-0.1440.9330.9381.0000.9040.9060.8980.8740.8120.9830.9830.9830.7820.6170.5700.4380.1650.2010.0000.0000.0060.0690.782-0.1490.823
climate_7d_mean_temp0.1020.0000.6390.102-0.1900.3120.3010.9810.9700.9041.0000.9450.9910.9870.8800.9860.9860.9860.8250.6560.4580.414-0.1680.1640.0000.000-0.3350.3120.8250.1900.539
climate_daily_max_temp0.2560.0000.8650.256-0.2390.0720.3100.9600.9510.9060.9451.0000.9700.9450.9400.9860.9860.9860.8310.7180.5840.527-0.125-0.1990.0000.000-0.3100.0720.8310.2080.703
climate_daily_mean_temp0.1590.0000.8270.159-0.2040.3180.3370.9870.9720.8980.9910.9701.0000.9900.9070.9860.9860.9860.9910.6910.4910.512-0.0880.0550.0000.000-0.3610.3180.9910.2190.908
climate_daily_min_temp0.1590.0000.8590.159-0.1920.3180.3370.9810.9730.8740.9870.9450.9901.0000.8860.9800.9800.9800.9730.6690.4360.516-0.0880.0550.0000.000-0.4250.3180.9730.2190.870
climate_heat_stress_index0.2020.0000.6350.202-0.243-0.1510.1110.8640.8430.8120.8800.9400.9070.8861.0000.9860.9860.9860.8590.5790.5780.389-0.098-0.2790.0000.000-0.296-0.1510.8590.0980.772
climate_p90_threshold1.0000.0000.3671.0000.3261.0001.0000.9830.6700.9830.9860.9860.9860.9800.9861.0000.9860.9860.6740.9860.7900.3311.0001.0000.0890.0890.9801.0000.6741.0000.259
climate_p95_threshold1.0000.0000.3671.0000.3261.0001.0000.9830.6700.9830.9860.9860.9860.9800.9860.9861.0000.9860.6740.9860.7900.3311.0001.0000.0890.0890.9801.0000.6741.0000.259
climate_p99_threshold1.0000.0000.3671.0000.3261.0001.0000.9830.6700.9830.9860.9860.9860.9800.9860.9860.9861.0000.6740.9860.7900.3311.0001.0000.0890.0890.9801.0000.6741.0000.259
climate_season0.0000.0000.8040.0000.1320.1690.2180.8620.7520.7820.8250.8310.9910.9730.8590.6740.6740.6741.0000.8600.6500.4410.0000.0800.1040.1040.9600.1691.0000.3020.901
climate_standardized_anomaly0.2560.0000.7810.256-0.1870.0720.3100.6900.6850.6170.6560.7180.6910.6690.5790.9860.9860.9860.8601.0000.8070.373-0.125-0.1990.0000.000-0.1770.0720.8600.2080.986
climate_temp_anomaly-0.0050.0000.798-0.005-0.247-0.300-0.1400.4730.4620.5700.4580.5840.4910.4360.5780.7900.7900.7900.6500.8071.0000.4680.020-0.2040.0000.0000.249-0.3000.650-0.1050.680
coordinate_source0.0000.0000.7090.0000.0000.0000.0000.4820.4740.4380.4140.5270.5120.5160.3890.3310.3310.3310.4410.3730.4681.0000.0000.0000.9970.9970.4200.0000.4410.2290.436
heart_rate_bpm0.1870.0001.0000.187-0.275-0.0200.294-0.047-0.0470.165-0.168-0.125-0.088-0.088-0.0981.0001.0001.0000.000-0.1250.0200.0001.000-0.1401.0001.0000.084-0.0200.0000.1230.000
height_m-0.2980.0001.000-0.2980.160-0.014-0.0970.0340.0340.2010.164-0.1990.0550.055-0.2791.0001.0001.0000.080-0.199-0.2040.000-0.1401.0001.0001.0000.220-0.0140.0800.0910.206
jhb_subregion1.0000.0000.0001.0000.0001.0001.0000.0000.0720.0000.0000.0000.0000.0000.0000.0890.0890.0890.1040.0000.0000.9971.0001.0001.0000.8710.0001.0000.1041.0000.000
longitude1.0000.0000.0001.0000.0001.0001.0000.0000.0720.0000.0000.0000.0000.0000.0000.0890.0890.0890.1040.0000.0000.9971.0001.0000.8711.0000.0001.0000.1041.0000.000
month-0.2480.0000.812-0.248-0.055-0.059-0.288-0.316-0.3090.006-0.335-0.310-0.361-0.425-0.2960.9800.9800.9800.960-0.1770.2490.4200.0840.2200.0000.0001.000-0.0590.960-0.1930.980
respiration rate0.0050.0001.0000.005-0.0201.0000.3270.3060.3060.0690.3120.0720.3180.318-0.1511.0001.0001.0000.1690.072-0.3000.000-0.020-0.0141.0001.000-0.0591.0000.1690.0080.114
season0.0000.0000.8040.0000.1320.1690.2180.8620.7520.7820.8250.8310.9910.9730.8590.6740.6740.6741.0000.8600.6500.4410.0000.0800.1040.1040.9600.1691.0000.3020.901
weight_kg0.9100.0621.0000.9100.1820.008-0.1420.2040.204-0.1490.1900.2080.2190.2190.0981.0001.0001.0000.3020.208-0.1050.2290.1230.0911.0001.000-0.1930.0080.3021.0000.228
year0.0000.0000.7170.0000.0580.1140.2530.6970.6090.8230.5390.7030.9080.8700.7720.2590.2590.2590.9010.9860.6800.4360.0000.2060.0000.0000.9800.1140.9010.2281.000

Missing values

2025-11-25T00:06:56.700630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-25T00:06:56.822130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-25T00:06:56.942835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

study_sourceprimary_dateyearmonthseasonlatitudelongitudejhb_subregioncityprovincecountrydateCountryClinical Study IDPotassium (mEq/L)respiration rateOther measures of obesitycoordinate_sourcecoordinate_precisiongeographic_sourceHEAT_VULNERABILITY_SCOREHEAT_STRESS_RISK_CATEGORYHIV_statusjohannesburg_metro_validstudy_site_locationBMI (kg/m²)Respiratory rate (breaths/min)climate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_7d_mean_tempclimate_7d_max_tempclimate_14d_mean_tempclimate_30d_mean_tempclimate_temp_anomalyclimate_standardized_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_p90_thresholdclimate_p95_thresholdclimate_p99_thresholdclimate_seasonheart_rate_bpmbody_temperature_celsiusweight_kgheight_msa_biomarker_standardscd4_correction_appliedfinal_comprehensive_fix_appliedtotal_protein_extreme_flagdphru_053_final_corrections_appliedezin_002_final_corrections_appliedquality_harmonization_versionwaist_circ_unit_correction_applied
1983JHB_EZIN_0252020-10-152020.010.0Spring-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2020-10-15South AfricaArm A3.520.018.577037JHB_VIDA_008highharmonized_datasets0.262454LOWPositive1.0Johannesburg (General)18.57703720.018.28525.65610.03818.60231.09418.83419.1026.554-0.2040.00.019.34328.40929.70431.797Spring83.036.558.21.771.00.01.00.00.00.02.0False
1984JHB_EZIN_0252020-10-282020.010.0Spring-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2020-10-28South AfricaArm B4.622.020.822066JHB_VIDA_008highharmonized_datasets0.262454LOWPositive1.0Johannesburg (General)20.82206622.018.28525.65610.03818.60231.09418.83419.1026.554-0.2040.00.019.34328.40929.70431.797Spring72.036.161.61.721.00.01.00.00.00.02.0False
1985JHB_EZIN_0252020-10-292020.010.0Spring-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2020-10-29South AfricaArm A4.919.022.308150JHB_VIDA_008highharmonized_datasets0.262454LOWPositive1.0Johannesburg (General)22.30815019.018.28525.65610.03818.60231.09418.83419.1026.554-0.2040.00.019.34328.40929.70431.797Spring84.036.360.01.641.00.01.00.00.00.02.0False
1986JHB_EZIN_0252020-11-042020.011.0Spring-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2020-11-04South AfricaArm C4.319.019.829482JHB_VIDA_008highharmonized_datasets0.262454LOWPositive1.0Johannesburg (General)19.82948219.018.35223.42112.69718.18728.84019.96620.5282.893-0.3360.00.017.42428.40929.70431.797Spring74.036.751.41.611.00.01.00.00.00.02.0False
1987JHB_EZIN_0252020-11-052020.011.0Spring-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2020-11-05South AfricaArm A4.720.025.307622JHB_VIDA_008highharmonized_datasets0.262454LOWPositive1.0Johannesburg (General)25.30762220.018.35223.42112.69718.18728.84019.96620.5282.893-0.3360.00.017.42428.40929.70431.797Spring105.037.168.91.651.00.01.00.00.00.02.0False
1988JHB_EZIN_0252020-12-092020.012.0Summer-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2020-12-09South AfricaArm B4.918.032.841490JHB_VIDA_008highharmonized_datasets0.262454LOWPositive1.0Johannesburg (General)32.84149018.019.33123.88915.38320.22129.21219.40919.5444.345-0.2720.00.016.35728.40929.70431.797Summer96.036.3112.41.851.00.01.00.00.00.02.0False
1989JHB_EZIN_0252020-12-112020.012.0Summer-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2020-12-11South AfricaArm E4.419.017.506390JHB_VIDA_008highharmonized_datasets0.262454LOWPositive1.0Johannesburg (General)17.50639019.019.33123.88915.38320.22129.21219.40919.5444.345-0.2720.00.016.35728.40929.70431.797Summer64.037.050.01.691.00.01.00.00.00.02.0False
1990JHB_EZIN_0252020-12-152020.012.0Summer-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2020-12-15South AfricaArm D4.918.031.313449JHB_VIDA_008highharmonized_datasets0.262454LOWPositive1.0Johannesburg (General)31.31344918.019.33123.88915.38320.22129.21219.40919.5444.345-0.2720.00.016.35728.40929.70431.797Summer50.036.2109.51.871.00.01.00.00.00.02.0False
1991JHB_EZIN_0252021-01-052021.01.0Summer-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-01-05South AfricaArm B4.419.031.633715JHB_VIDA_008highharmonized_datasets0.262454LOWPositive1.0Johannesburg (General)31.63371519.021.62626.90216.62019.78827.18320.75120.8556.0470.8430.00.020.12928.40929.70431.797Summer80.036.476.01.551.00.01.00.00.00.02.0False
1992JHB_EZIN_0252021-01-052021.01.0Summer-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-01-05South AfricaArm C4.120.039.101562JHB_VIDA_008highharmonized_datasets0.262454LOWPositive1.0Johannesburg (General)39.10156220.021.62626.90216.62019.78827.18320.75120.8556.0470.8430.00.020.12928.40929.70431.797Summer70.036.3100.11.601.00.01.00.00.00.02.0False
study_sourceprimary_dateyearmonthseasonlatitudelongitudejhb_subregioncityprovincecountrydateCountryClinical Study IDPotassium (mEq/L)respiration rateOther measures of obesitycoordinate_sourcecoordinate_precisiongeographic_sourceHEAT_VULNERABILITY_SCOREHEAT_STRESS_RISK_CATEGORYHIV_statusjohannesburg_metro_validstudy_site_locationBMI (kg/m²)Respiratory rate (breaths/min)climate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_7d_mean_tempclimate_7d_max_tempclimate_14d_mean_tempclimate_30d_mean_tempclimate_temp_anomalyclimate_standardized_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_p90_thresholdclimate_p95_thresholdclimate_p99_thresholdclimate_seasonheart_rate_bpmbody_temperature_celsiusweight_kgheight_msa_biomarker_standardscd4_correction_appliedfinal_comprehensive_fix_appliedtotal_protein_extreme_flagdphru_053_final_corrections_appliedezin_002_final_corrections_appliedquality_harmonization_versionwaist_circ_unit_correction_applied
2152JHB_EZIN_0252021-06-152021.06.0Winter-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-06-15South AfricaArm B4.2NaNNaNJHB_EZIN_025highharmonized_datasets0.0LOWPositive1.0Johannesburg (General)NaNNaN7.09813.1471.4688.24418.3449.44310.6132.534-1.2460.00.07.39328.24629.51331.748WinterNaNNaNNaNNaN1.00.01.00.00.00.02.0False
2153JHB_EZIN_0252021-06-162021.06.0Winter-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-06-16South AfricaArm B5.1NaNNaNJHB_EZIN_025highharmonized_datasets0.0LOWPositive1.0Johannesburg (General)NaNNaN7.09813.1471.4688.24418.3449.44310.6132.534-1.2460.00.07.39328.24629.51331.748WinterNaNNaNNaNNaN1.00.01.00.00.00.02.0False
2154JHB_EZIN_0252021-07-012021.07.0Winter-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-07-01South AfricaArm E4.7NaNNaNJHB_EZIN_025highharmonized_datasets0.0LOWPositive1.0Johannesburg (General)NaNNaN11.38118.2004.96412.94622.61411.88310.9787.2220.6710.00.011.32328.40929.70431.797WinterNaNNaNNaNNaN1.00.01.00.00.00.02.0False
2155JHB_EZIN_0252021-07-012021.07.0Winter-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-07-01South AfricaArm D3.8NaNNaNJHB_EZIN_025highharmonized_datasets0.0LOWPositive1.0Johannesburg (General)NaNNaN11.38118.2004.96412.94622.61411.88310.9787.2220.6710.00.011.32328.40929.70431.797WinterNaNNaNNaNNaN1.00.01.00.00.00.02.0False
2156JHB_EZIN_0252021-05-132021.05.0Autumn-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-05-13South AfricaArm A5.6NaNNaNJHB_VIDA_008highharmonized_datasets0.0LOWPositive1.0Johannesburg (General)NaNNaN13.59919.9787.47313.29421.40514.09714.5195.4580.3100.00.016.13428.40929.70431.797AutumnNaNNaNNaNNaN1.00.01.00.00.00.02.0False
2157JHB_EZIN_0252021-05-202021.05.0Autumn-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-05-20South AfricaArm B4.3NaNNaNJHB_VIDA_008highharmonized_datasets0.0LOWPositive1.0Johannesburg (General)NaNNaN13.59919.9787.47313.29421.40514.09714.5195.4580.3100.00.016.13428.40929.70431.797AutumnNaNNaNNaNNaN1.00.01.00.00.00.02.0False
2158JHB_EZIN_0252021-05-272021.05.0Autumn-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-05-27South AfricaArm D4.7NaNNaNJHB_VIDA_008highharmonized_datasets0.0LOWPositive1.0Johannesburg (General)NaNNaN13.59919.9787.47313.29421.40514.09714.5195.4580.3100.00.016.13428.40929.70431.797AutumnNaNNaNNaNNaN1.00.01.00.00.00.02.0False
2159JHB_EZIN_0252021-06-082021.06.0Winter-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-06-08South AfricaArm E5.7NaNNaNJHB_VIDA_008highharmonized_datasets0.0LOWPositive1.0Johannesburg (General)NaNNaN7.09813.1471.4688.24418.3449.44310.6132.534-1.2460.00.07.39328.24629.51331.748WinterNaNNaNNaNNaN1.00.01.00.00.00.02.0False
2160JHB_EZIN_0252021-06-082021.06.0Winter-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-06-08South AfricaArm A5.4NaNNaNJHB_VIDA_008highharmonized_datasets0.0LOWPositive1.0Johannesburg (General)NaNNaN7.09813.1471.4688.24418.3449.44310.6132.534-1.2460.00.07.39328.24629.51331.748WinterNaNNaNNaNNaN1.00.01.00.00.00.02.0False
2161JHB_EZIN_0252021-06-082021.06.0Winter-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-06-08South AfricaArm C4.9NaNNaNJHB_VIDA_008highharmonized_datasets0.0LOWPositive1.0Johannesburg (General)NaNNaN7.09813.1471.4688.24418.3449.44310.6132.534-1.2460.00.07.39328.24629.51331.748WinterNaNNaNNaNNaN1.00.01.00.00.00.02.0False